Animatronic shader lamps avatars

Applications such as telepresence and training involve the display of real or synthetic humans to multiple viewers. When attempting to render the humans with conventional displays, non-verbal cues such as head pose, gaze direction, body posture, and facial expression are difficult to convey correctly to all viewers. In addition, a framed image of a human conveys only a limited physical sense of presence—primarily through the display’s location. While progress continues on articulated robots that mimic humans, the focus has been on the motion and behavior of the robots rather than on their appearance. We introduce a new approach for robotic avatars of real people: the use of cameras and projectors to capture and map both the dynamic motion and the appearance of a real person onto a humanoid animatronic model. We call these devices animatronic Shader Lamps Avatars (SLA). We present a proof-of-concept prototype comprised of a camera, a tracking system, a digital projector, and a life-sized styrofoam head mounted on a pan-tilt unit. The system captures imagery of a moving, talking user and maps the appearance and motion onto the animatronic SLA, delivering a dynamic, real-time representation of the user to multiple viewers.     

Free-living inferential modeling of blood glucose level using only noninvasive inputs

The goal of this work is to present a causation modeling methodology with the ability to accurately infer blood glucose levels using a large set of highly correlated noninvasive input variables over an extended period of time. These models can provide insight to improve glucose monitoring, and glucose regulation through advanced model-based control technologies. The efficacy of this approach is demonstrated using real data from a type 2 diabetic (T2D) subject collected under free-living conditions over a period of 25 consecutive days. The model was identified and tested using eleven variables that included three food variables as well as several activity and stress variables. The model was trained using 20 days of data and validated using 5 days of data. This gave a fitted correlation coefficient of 0.70 and an average absolute error (AAE) (i.e., the average of the absolute values for the measured glucose concentration minus modeled glucose concentration) of 13.3 mg/dL for the validation data. This AAE result was significantly better than the subject’s personal glucose meter AAE of 15.3 mg/dL for replicated measurements.     

Strategy, networks and systems in the global translation services market

The globalisation of markets has led to an increased demand for language translation services that support and enable communication between economic partners. For example, technical documents, software systems, business documents and web sites all need to be translated into multiple languages for individual national markets, and the information that they contain changes periodically. This paper sets out a theoretical framework that describes the architectures of business processes within and between separate firms that are used to support the delivery and management of services. This is done by coordinating the fit between externally generated problem complexity, from customers, and the internally generated complexity of different potential network configuration solutions. The theoretical framework is an architecture of how complexity is generated and managed at the different structural levels and across the different processual stages of an industry. A case study of thebigword, a major international translation services company, illustrates how the framework is applied in practice. The case study analyses the implementation of an electronic market platform which enables the coordination of the different stakeholders involved in the translation services market. These stakeholders include translators, translation services companies and their clients in a global business network.     

Green laser sources optimized for highly efficient microdisplay‐based field‐sequential mobile projectors

Abstract— A novel green laser source, based on a monolithic cavity microchip laser platform, has been developed. The laser is designed to be a part of a miniature and efficient RGB light source for microdisplay‐based mobile projector devices. The use of highly efficient, periodically poled MgO‐doped lithium niobate as the non‐linear frequency doubler allows for a significant increase in the overall efficiency of the green microchip laser. Specifically, a 50–150‐mW green output with a wall‐plug efficiency exceeding 10% in the temperature range of greater than 40°C has been demonstrated. A compact package for this laser source with a volume less than 0.33 cm³ is discussed and results of performance tests are presented.     

Fluid Simulation with Articulated Bodies

We present an algorithm for creating realistic animations of characters that are swimming through fluids. Our approach combines dynamic simulation with data-driven kinematic motions (motion capture data) to produce realistic animation in a fluid. The interaction of the articulated body with the fluid is performed by incorporating joint constraints with rigid animation and by extending a solid/fluid coupling method to handle articulated chains. Our solver takes as input the current state of the simulation and calculates the angular and linear accelerations of the connected bodies needed to match a particular motion sequence for the articulated body. These accelerations are used to estimate the forces and torques that are then applied to each joint. Based on this approach, we demonstrate simulated swimming results for a variety of different strokes, including crawl, backstroke, breaststroke, and butterfly. The ability to have articulated bodies interact with fluids also allows us to generate simulations of simple water creatures that are driven by simple controllers.     

Hume box calculus: robust system development through software transformation

Hume is a contemporary programming language oriented to systems with strong resource bounds, based on autonomous concurrent “boxes” interacting across “wires”. Hume’s design reflects the explicit separation of coordination and computation aspects of multi-process systems, which greatly eases establishing resource bounds for programs. However, coordination and computation are necessarily tightly coupled in reasoning about Hume programs. Furthermore, in Hume, local changes to coordination or computation, while preserving input/output correctness, can have profound and unforeseen effects on other aspects of programs such as timing of events and scheduling of processes. Thus, traditional program calculi prove inappropriate as they tend to focus exclusively either on the coordination of interacting processes or on computation within individual processes.     

Situation awareness acquired from monitoring process plants – the Process Overview concept and measure

We introduce Process Overview, a situation awareness characterisation of the knowledge derived from monitoring process plants. Process Overview is based on observational studies of process control work in the literature. The characterisation is applied to develop a query-based measure called the Process Overview Measure. The goal of the measure is to improve coupling between situation and awareness according to process plant properties and operator cognitive work. A companion article presents the empirical evaluation of the Process Overview Measure in a realistic process control setting. The Process Overview Measure demonstrated sensitivity and validity by revealing significant effects of experimental manipulations that corroborated with other empirical results. The measure also demonstrated adequate inter-rater reliability and practicality for measuring SA based on data collected by process experts.

Practitioner Summary: The Process Overview Measure is a query-based measure for assessing operator situation awareness from monitoring process plants in representative settings.     

Reliable representations for association rules

Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we first propose a definition for redundancy, then propose a concise representation, called a Reliable basis, for representing non-redundant association rules. The Reliable basis contains a set of non-redundant rules which are derived using frequent closed itemsets and their generators instead of using frequent itemsets that are usually used by traditional association rule mining approaches. An important contribution of this paper is that we propose to use the certainty factor as the criterion to measure the strength of the discovered association rules. Using this criterion, we can ensure the elimination of as many redundant rules as possible without reducing the inference capacity of the remaining extracted non-redundant rules. We prove that the redundancy elimination, based on the proposed Reliable basis, does not reduce the strength of belief in the extracted rules. We also prove that all association rules, their supports and confidences, can be retrieved from the Reliable basis without accessing the dataset. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules. We also conduct experiments on the application of association rules to the area of product recommendation. The experimental results show that the non-redundant association rules extracted using the proposed method retain the same inference capacity as the entire rule set. This result indicates that using non-redundant rules only is sufficient to solve real problems needless using the entire rule set.     

Modeling the glucose regulatory system in extreme preterm infants

Background

Premature infants represent a significant proportion of the neonatal intensive care population. Blood glucose homeostasis in this group is often disturbed by immaturity of endogenous regulatory systems and the stress of their condition. Hypo- and hyperglycemia are frequently reported in very low birth weight infants, and more mature infants often experience low levels of glycemia. A model capturing the unique fundamental dynamics of the neonatal glucose regulatory system could be used to develop better blood glucose control methods.

Methods

A metabolic system model is adapted from adult critical care to the unique physiological case of the neonate. Integral-based fitting methods were used to identify time-varying insulin sensitivity and non-insulin mediated glucose uptake profiles. The clinically important predictive ability of the model was assessed by assuming insulin sensitivity was constant over prediction intervals of 1, 2 and 4 h forward and comparing model-simulated versus actual clinical glucose values for all recorded interventions. The clinical data included 1091 glucose measurements over 3567 total patient hours, along with all associated insulin and nutritional infusion data, for N = 25 total cases. Ethics approval was obtained from the Upper South A Regional Ethics Committee for this study.

Results

The identified model had a median absolute percentage error of 2.4% [IQR: 0.9-4.8%] between model-fitted and clinical glucose values. Median absolute prediction errors at 1-, 2- and 4-h intervals were 5.2% [IQR: 2.5-10.3%], 9.4% [IQR: 4.5-18.4%] and 13.6% [IQR: 6.3-27.6%] respectively.

Conclusions

The model accurately captures and predicts the fundamental dynamic behaviors of the neonatal metabolism well enough for effective clinical decision support in glycemic control. The adaptation from adult to a neonatal case is based on the data from the literature. Low prediction errors and very low fitting errors indicate that the fundamental dynamics of glucose metabolism in both premature neonates and critical care adults can be described by similar mathematical models.     

Buyer-supplier games: Optimization over the core

In a buyer-supplier game, a special type of assignment game, a distinguished player, called the buyer, wishes to purchase some combinatorial structure. A set of players, called suppliers, offer various components of the structure for sale. Any combinatorial minimization problem can be transformed into a buyer-supplier game. While most previous work has been concerned with characterizing the core of buyer-supplier games, in this paper we study optimization over the set of core vectors. We give a polynomial time algorithm for optimizing over the core of any buyer-supplier game for which the underlying minimization problem is solvable in polynomial time. In addition, we show that it is hard to determine whether a given vector belongs to the core if the base minimization problem is not solvable in polynomial time. Finally, we introduce and study the concept of focus point price, which answers the question: If we are constrained to play in equilibrium, how much can we lose by playing the wrong equilibrium?